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Military Standards Database
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Received: February 28, 2022 / Reviewed: April 11, 2022 / Accepted: April 19, 2022 / Published: April 22, 2022
(This article belongs to the special issue Applications of Data Mining in Computer Decision Support Systems and Other Related Aspects)
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The military environment generates large amounts of data of great importance, which makes it necessary to use machine learning for its processing. The ability to learn and predict possible scenarios by analyzing the large volume of data generated provides automated learning and decision support. This article aims to present a machine learning architecture model applied to a military organization, which is driven and supported by a bibliometric study applied to a non-military organization architecture model. For this purpose, a bibliometric analysis was carried out until 2021, a strategy diagram was prepared and the results were interpreted. The information used was extracted from the ISI WoS, one of the main databases widely accepted by the scientific community. No direct military sources were used. This work is divided into five parts: a study of previous research related to machine learning in the military world; A description of our research methodology using SciMat, Excel and VosViewer tools; Use of this method based on data mining, pre-processing, cluster normalization, strategic mapping and analysis of its results to investigate machine learning in a military context; Based on these results, a conceptual architecture of the practical use of ML in a military context was created; And finally, we present the conclusions, where we analyze the main areas and recent advances in machine learning, in this case, for military environments, to analyze large amounts of data, provide usability, machine learning and decision support.
Machine learning (ML) allows for the automation of many tasks, leveraging the vast amount of information available from various sources, including big data applications. Currently, its use is widespread and ML is an important part of our daily life [1].
In the military, the use of smart applications has accelerated [2]. For example, South Korea's Ministry of National Defense has significantly increased its intelligence, and with fewer and fewer intelligence analysts, they have to apply artificial intelligence (AI) technology to process all information accurately and on time [3]. Another example to note is the dependence on oil for military equipment and machinery. This is where ML comes in, as military logistics must rely on intelligently informed deductions [4]; So we see how ML is integrated into the military world.
The purpose of this article is to present an architectural model that reflects how ML is applied in a practical way in a military environment. In this architecture, we approach elements such as the most frequent data, algorithms and applications used in a military context.
Deployment And Reimbursement
In carrying out this work, as we will see in Section 2, we studied related works, noting that there are few review works on this emerging topic, which has aroused our interest in carrying out bibliometric analyzes on the Web, one of the main scientific databases. of Science, up to and including 2021. In the same section, since there are no works that reflect such architecture in the military domain, we present the ML in a practical way.
The bibliometric method used in this work is described in Section 3, and we mainly use the SciMat bibliometric analysis tool, which is capable of performing scientific mapping analysis on a longitudinal structure [5]. With this analysis we build a strategy diagram that identifies the main areas of ML applied to the military field.
In Section 4, we apply the described approach to performing analysis by source: we look at the main scientific areas where ML is applied to the military world; Author and Reference: We determine who is the most active author on the subject; Country: In this sense, we analyze whether the countries that produce the most scientific documentation are generally the countries with the fewest citations; And we distinguish two periods: before 2015 and after 2016, the increase in publications about ML in the military world increases rapidly.
In Section 5, having completed this bibliometric analysis, we are now in a position to redefine the conceptual architecture presented in Section 2 specifically for military organizations.
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Finally, we reach some conclusions, in which we reveal the main thematic areas that we found and the results related to the conclusions.
First, in Section 2.1, we searched for bibliographic or review articles related to the application of ML in the military world, and found no relevant information. Then, in Section 2.2, we look for a data-driven architecture for non-military organizations that serves as a basis for establishing a new model oriented to the military world, which we complement with the present bibliometric study.
In this section, we study review articles or bibliometric studies on AI (which includes ML) and related fields, along with their applications in the military field. The results of this study are shown in Table 1.
Due to your interest, we analyze this last group in more detail. First, we find recent review papers on specific optimization techniques such as dynamic programming and their application in the military field [34].
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We also found a review of AI and its applications in the military field [35]. This review describes three major military applications:
While this work is interesting, it is not specifically oriented towards ML and the US. It mainly focuses on apps. In this sense, it cannot be considered a global survey of the area.
There are other less important NATO (North Atlantic Treaty Organization) documents that coincide with military applications of AI [31].
It is noticed that the existing scientific literature does not reflect specific and comprehensive studies on ML applied to military environments. Thus, we see the need to carry out this innovative study that provides the scientific community with updated data on the scientific interest of ML applied to military environments, describes the identified areas and makes their classification, and shows the existing interest in making decisions based on data in such military environments. In Section 3 we describe the methodology used for this study, which intends to be more objective than most related works by using a bibliometric study as a basis.
What Makes Something Military Grade
Data-driven decision making has ML algorithms as its core components. There are works that specify conceptual architectures that allow the organization to adopt this philosophy in a practical way in a scalable context [37], which allows adaptation to the big data environment, that is, with a growing volume of data and various formats generated ( not structured, semi-structured and structured); Therefore, they must be processed at high speed. We did not find specific architectures for military organizations in the literature: therefore, we specify in Figure 1, a general architecture based on [37, 38], which supports these types of organizations in the context of big data discussed. As mentioned, the main objective of this work is to specify in more detail such architectural elements for a military organization.
Data Management Solutions for Business Analytics (DMSBA). Data is the essential raw material for data-driven decision-making. From them, ML algorithms are able to discover the desired knowledge in the form of patterns. So, we must have
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